{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T18:04:40Z","timestamp":1777658680721,"version":"3.51.4"},"reference-count":0,"publisher":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial","issue":"74","license":[{"start":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T00:00:00Z","timestamp":1716681600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ia"],"abstract":"<jats:p>One of the most prevalent forms of cancer worldwide is skin cancer. Determining disease characteristics necessitates a clinical evaluation of skin lesions, but this process is limited by long time horizons and a multiplicity of interpretations. Deep learning techniques have been created to help dermatologists with these issues as a higher patient survival rate depends on the early and precise detection of skin cancer. This research proposed a new approach for binary classification of dermoscopic images for skin cancer. The Improved Grey Wolf Optimizer (I-GWO) is used in this technique to fine-tune some hyperparameters\u2019 values of various pre-trained deep learning networks to maximize results. SqueezeNet, ShuffleNet, AlexNet, ResNet-18, and DarkNet-19 are the pre-trained networks that were employed. We tested the MED-NODE and DermIS databases in our investigation. Concerning the MED-NODE and DermIS datasets, the proposed method's highest accuracy results are 100% and 97%, respectively.<\/jats:p>","DOI":"10.4114\/intartif.vol27iss74pp102-116","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T04:59:39Z","timestamp":1716785979000},"page":"102-116","source":"Crossref","is-referenced-by-count":3,"title":["Binary Classification of Skin Cancer Images Using Pre-trained Networks with I-GWO"],"prefix":"10.4114","volume":"27","author":[{"given":"Hadeer","family":"Hussein","sequence":"first","affiliation":[]},{"family":"Ahmed Magdy","sequence":"additional","affiliation":[]},{"given":"Rehab F.","family":"Abdel-Kader","sequence":"additional","affiliation":[]},{"family":"Khaled Abd El Salam","sequence":"additional","affiliation":[]}],"member":"2598","published-online":{"date-parts":[[2024,5,26]]},"container-title":["Inteligencia Artificial"],"original-title":[],"link":[{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1411\/226","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1411\/226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T04:59:41Z","timestamp":1716785981000},"score":1,"resource":{"primary":{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/view\/1411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,26]]},"references-count":0,"journal-issue":{"issue":"74","published-online":{"date-parts":[[2024,5,17]]}},"URL":"https:\/\/doi.org\/10.4114\/intartif.vol27iss74pp102-116","relation":{},"ISSN":["1988-3064","1137-3601"],"issn-type":[{"value":"1988-3064","type":"electronic"},{"value":"1137-3601","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5,26]]}}}